CN112149543B - Building dust recognition system and method based on computer vision - Google Patents

Building dust recognition system and method based on computer vision Download PDF

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CN112149543B
CN112149543B CN202010973045.2A CN202010973045A CN112149543B CN 112149543 B CN112149543 B CN 112149543B CN 202010973045 A CN202010973045 A CN 202010973045A CN 112149543 B CN112149543 B CN 112149543B
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image
building
dust
building dust
pixel
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CN112149543A (en
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雷飞
马晓鹤
董学应
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Beijing University of Technology
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/50Context or environment of the image
    • G06V20/52Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/20Image preprocessing
    • G06V10/26Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds

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Abstract

The invention discloses a building dust recognition system and method based on computer vision. And performing color model conversion on the processed image. The binary image morphological opening operation is performed, corrosion is performed first, then expansion is performed, the influence of fine objects is eliminated, the edge area is enlarged, the background and foreground images are obtained, and the background and foreground images are combined to form a mask image. And calculating the ratio of the pixel area of the extracted building dust to the total pixel area of the image by using a pixel area method, and judging whether the image has the building dust. The image has a stored image of building dust and outputs an identification alarm signal. The invention is a system for automatically completing the automatic identification and alarm of building dust by combining computer vision, image processing and modern communication technology, solves the defects of long measurement period, poor precision and the like of the existing dust detector, and is beneficial to the monitoring work of management staff.

Description

Building dust recognition system and method based on computer vision
Technical Field
The patent relates to computer vision and image processing technology, in particular to an automatic recognition system for building dust by using a filtering process, image model conversion, channel separation, global threshold binarization, morphological corrosion and expansion, a watershed algorithm, dust recognition and an alarm signal output method.
Background
Dust particles are generated in each stage of building construction, so that real-time continuous monitoring of dust is needed. At present, the domestic dust monitoring method mainly comprises a dust detector taking an optical sensor as a counter, and has the advantages of large measurement error, long period and poor stability, and many monitoring methods can not timely give measurement results. Along with the rapid development of image processing and computer vision, the invention provides a building dust recognition method combining computer vision, image processing and modern communication technology, which can detect building dust targets in time and rapidly alarm, and overcomes the defects of building dust detection in the prior art. The wireless cameras with different angles are installed on a construction site, single-frame monitoring images are acquired by the cameras with different angles according to fixed time of each day, and the single-frame monitoring images are transmitted to an image information base through a wireless network. The method comprises the steps of calling an image of an information base, running VS2010 software to process the image, and judging whether a large-area dust pollution exists in a construction site or not by utilizing computer vision. If dust pollution exists, giving an alarm prompt, and reminding a manager of the construction site of carrying out dust fall measures. The method is based on a dust pollution recognition and recognition signal output mechanism of the monitoring terminal, is simple to operate, liberates a large amount of manual labor, and effectively reduces the maintenance cost of the instrument.
Disclosure of Invention
The invention provides a building dust recognition method by combining computer vision, image processing and modern communication technology. The defect that the existing building dust monitoring is time-consuming and labor-consuming and the measurement error is large is overcome. The method is simple to operate, economical and efficient, can sensitively and rapidly obtain the image information of the construction region, judges whether building dust is generated or not, gives out alarm identification signals, and brings great convenience to personnel monitoring the building dust.
In order to achieve the purpose, the technical scheme adopted by the invention is a building dust recognition scheme based on computer vision:
the building dust recognition system based on computer vision mainly comprises a building site image acquisition module, a building site image information storage module, a building site image processing module, a display for displaying building site image information and an alarm module. The building site image acquisition module is connected with the building site image information storage module, and the building site image information storage module is connected with the building site image processing module; the building site image processing module is respectively connected with the display and the alarm module; the image acquisition is carried out by acquiring images of surrounding environment of the building site at fixed time every day by wireless network cameras arranged at all angles of the building site, transmitting the images to an information storage module through a wireless network, storing image information, filtering and algorithmic analysis by an image processing module, and sending an alarm signal when the input image information reaches an alarm threshold value. Timely and efficient identification information is provided for monitoring personnel, and identification alarm signals are output in real time, so that the operation and maintenance cost of monitoring building dust is greatly reduced.
The monitoring, identification and alarm signal output of building dust in building site image is mainly characterized by that a series of building site images are undergone the process of image processing to obtain identification target, the target is undergone the process of calculation of pixel point proportion, and finally the output alarm signal is judged.
The steps are as follows:
the method comprises the steps of a), transmitting a single frame image of a construction site acquired by an image acquisition module to an image information storage module, and calling the single frame image of the image information storage module to perform image initialization processing, wherein the specific method is to adjust the size of the single frame image and the resolution of the image, and the formula is as follows:
pc=src.cols/dest.cols
pr=src.rows/dest.rows
scr.cols is the height of the original image, scr.rows is the width of the original image;
the dest.cols is the height of the adjusted image, and the dest.rows is the width of the adjusted image;
pc is the height scale, pr is the width scale;
the original image width and height are x×pc and y×pr respectively, and the output image width and height are x and y respectively, so that the newly adjusted image is not lost.
Step b) carrying out Gaussian filtering on the image with the adjusted size and resolution to retain the target characteristics of the image, and removing a large amount of noise pollution to the image in the processes of forming, transmitting and storing. The Gaussian filtering is a smoothing filter which carries out convolution operation on pixel points of an input image and a convolution module of a Gaussian kernel, carries out weighted average on the whole image and forms a filtered image array by one block of results. The weighting value is determined by the shape of a gaussian function, and the formula of the two-dimensional gaussian function is:
g (x, y) is the pixel value of the output image at the (x, y) point, and the distribution parameter sigma is the width of the filter;
x is the abscissa value of the pixel point, and y is the ordinate value of the pixel point;
the calculation formula is as follows by using the Gaussian kernel of 3×3:
g(x,y)={f(x-1,y-1)+f(x-1,y+1)+f(x+1,y-1)+f(x+1,y+1)+[f(x-1,y)+f(x,y-1)+f(x+1,y)+f(x,y+1)]*2+f(x,y)*4}/16;
g (x, y): outputting pixel values of the image at (x, y) points;
f (x, y): pixel values of the input image at (x, y) points;
and c) converting the image of the RGB color model after the two-dimensional Gaussian filtering into an image of an HSV color model, wherein the building dust feature in the image is more prominent after the image with building dust is converted into the HSV color model. The HSV color model is a color space of a hexagonal cone model, and parameters of the model include hue, saturation and brightness. The two model conversion relation calculation formulas are as follows:
V=max
max: representing the maximum value among the three channels of RGB;
min: representing the minimum in the three channels of RGB;
after the image in the step d) is converted into an HSV color model, the image of the HSV model is separated into images of three channels of hue, saturation and brightness. Finding out an optimal threshold interval according to the pixel value and the frequency distribution histogram of each channel image, adopting global threshold interval binarization, setting the pixel value of the pixel points of the traversing image in the threshold interval to be 255, and setting the pixel value not in the threshold period to be 0.
Step e), combining the binarized images of the three channels, and performing morphological opening operation on the newly generated images. Invoking the opencv functions erode and dialate erodes before expanding, and the open operation has the effect of eliminating fine objects, separating objects at the fine and smoothing the boundaries of larger objects.
The step of the corrosion algorithm:
(1) Scanning each element of the image with 3x3 structural elements
(2) AND operation with structural elements and binary images covered thereby
(3) If both are 1, the pixel of the resulting image is 1. Otherwise is 0
(4) Results: eliminating tiny meaningless noise points
The step of the expansion algorithm:
(1) Scanning each element of the image with 3x3 structural elements
(2) AND operation with structural elements and binary images covered thereby
(3) If both are 0, the pixel of the resulting image is 0. Otherwise is 1
(4) Results: filling the background void phenomenon.
And combining two foreground and background binarized images obtained by morphological opening operation into a 32-bit mask image, and dividing the target area by using the mask image as a mark.
And f) taking the combined mask image after corrosion and expansion treatment as a mark image of a watershed algorithm. The watershed algorithm is a mathematical morphology bottom-up recursion process based on topology theory, and is based on similarity between adjacent pixels as an important reference, so that pixels which are similar in spatial position and similar in gray value are connected with each other to form a closed contour so as to divide a dust object. The recursive formula is as follows:
representing the smallest pixel point of the gray value in the image I;
h min representing the minimum gray value in the image, h max The value with the maximum gray value;
X h+1 representing all pixel points, min on the gray value h+1 h+1 The minimum point of the gray value in the region is represented,is X h The point is in the region with gray value h and X h ∩X h+1 Representing the point where the two intersect, at the same region;
dividing the building dust-raising area by using a watershed algorithm, counting the number of the pixels of the dust-raising target area and the number of the pixels of the whole image by using a pixel area method, and calculating the ratio value of the number of the pixels of the dust-raising target area and the number of the pixels of the image. The formula is as follows:
scale_img=whitecount/pixekcount
while the whistecount is the number of pixels in the building dust-raising target area, the pixekcount is the number of total pixels in the whole image, and scale_img is the ratio value of the two;
and h) calculating a proportion value of the building dust area and the whole image, marking the image with the proportion value larger than a threshold value with a problematic label, carrying out target recognition on the building dust, and giving a target recognition alarm signal. The program stores the problematic photo, and is convenient for monitoring personnel to check information.
The invention aims to solve the defects of long detection period, poor precision and high maintenance cost of the existing building dust. A building dust recognition method combining computer vision, image processing and modern communication technology is provided. The method is economical and efficient, can sensitively and rapidly acquire the image information of the construction region, judges whether the construction dust exists or not, gives out alarm identification signals, and brings great convenience to personnel monitoring the construction dust.
Drawings
Fig. 1 is a flowchart of an algorithm.
Fig. 2 is a view of building dust target monitoring. a is an original drawing; b is a dust identification chart.
Fig. 3 is a graph of real-time monitoring signal output.
Detailed Description
Fig. 1 is a flow chart of overall design of building dust monitoring, and the core part is an image processing algorithm process. The pictures with building dust are subjected to target monitoring and output monitoring identification signals, and the experiments can achieve good effects. The specific implementation mode is as follows:
1. the single frame image acquired by the information base is initialized in size and resolution, the size of the image is set to 400 x 400, and the image is named as 'g_srchimage'. The initialized image is subjected to gaussian filtering processing, so that σ=0.7 in the gaussian function.
2. The preprocessed RGB image is converted into HSV model pictures, the pictures after model conversion are separated into three channels of pictures, and global binarization operation is carried out respectively. Three binarized pictures himg, simg, vimg are obtained and combined into one binarized picture binary_image.
3. Morphological opening operation is carried out on the binary_image, the opencv function erode corrosion operation is called for 6 times to obtain a foreground picture, and the function dialate expansion operation is called for 6 times to obtain a background picture. The foreground picture and the background picture form a mask picture marker, and the mask picture is used as a mark to extract a target area by using a watershed algorithm.
4. And calculating the proportion area of the dust raising area by adopting a pixel area method, marking a problem label when the proportion is larger than 0.06, storing a picture with building dust raising and outputting an alarm identification signal.

Claims (8)

1. The building dust recognition method based on computer vision comprises a building image acquisition module, a building image information storage module, a building image processing module, a display and an alarm module;
the building site image acquisition module is connected with the building site image information storage module, and the building site image information storage module is connected with the building site image processing module; the building site image processing module is respectively connected with the display and the alarm module;
in the building site image acquisition module, wireless network cameras arranged at all angles of a building site acquire images of surrounding environments of the building site at fixed moments every day; transmitting the image information to a building site image information storage module via a wireless network to store the image information; filtering and algorithmically analyzing by a building image processing module, and sending an alarm signal when the input building image information reaches an alarm threshold value; displaying building image information by a display;
the monitoring, identification and alarm signal output of building dust in building site images is mainly realized by performing image processing on a series of building site images to obtain an identification target, calculating the pixel point proportion of the target and finally judging the output alarm signal;
the method is characterized in that: the method is implemented as follows,
the method comprises the steps that a) an image acquisition module acquires a single frame image of a construction site, transmits the single frame image to an image information storage module again, calls the single frame image of the image information storage module to perform image initialization processing, and adjusts the size of the single frame image and the resolution of the image;
step b), performing Gaussian filtering on the image with the adjusted size and resolution to retain target characteristics of the image, and removing a large amount of noise pollution on the image in the forming, transmitting and storing processes; the Gaussian filtering is a smoothing filter for carrying out convolution operation on pixel points of an input image and a convolution module of a Gaussian kernel, carrying out weighted average on the whole image and forming a filtered image array by the results;
step c), converting the image of the RGB color model after the two-dimensional Gaussian filtering into an image of an HSV color model, and after converting the image with building dust into the HSV color model, enabling the building dust characteristic in the image to be more prominent; the HSV color model is a color space of a hexagonal cone model, and parameters of the model include hue, saturation and brightness;
after the image in the step d) is converted into an HSV color model, separating the image of the HSV model into images of three channels of hue, saturation and brightness; finding out an optimal threshold interval according to the pixel value and the frequency distribution histogram of each channel image, adopting global threshold interval binarization, setting the pixel value of the pixel points of the traversal image in the threshold interval to be 255, and setting the pixel value not in the threshold period to be 0;
step e), combining the binarized images of the three channels, and performing morphological opening operation on the newly generated images; the open operation has the effects of eliminating fine objects, separating objects at the fine and smoothing the boundary of larger objects;
step f) the mask images combined after corrosion and expansion treatment are used as mark images of a watershed algorithm;
dividing a building dust-raising area by a watershed algorithm, counting the number of pixels in a dust-raising target area and the number of pixels in the whole image by adopting a pixel area method, and calculating the ratio value of the number of pixels in the dust-raising target area to the number of pixels in the image;
step h), calculating a proportion value of a building dust area and the whole image, marking the image with the proportion value larger than a threshold value with a problematic label, carrying out target recognition on building dust, and giving a target recognition alarm signal; the program stores the problematic photo, and is convenient for monitoring personnel to check information.
2. The building dust identification method based on computer vision according to claim 1, wherein the method comprises the following steps: and transmitting the single frame image of the construction site to an information base by using a camera, and adjusting the size of the image and the size of the resolution.
3. The building dust identification method based on computer vision according to claim 1, wherein the method comprises the following steps: noise pollution received in the image forming and transmitting process is filtered by Gaussian filtering, and isolated pixel points and pixel blocks are eliminated under the condition that the detail characteristics of the image are maintained.
4. The building dust identification method based on computer vision according to claim 1, wherein the method comprises the following steps: converting an RGB color model of an image into an HSV color model, and separating the image of the HSV color model into three channels of brightness, saturation and brightness; the global threshold of each channel image is binarized, and three binarized images are combined into one binarized image.
5. The building dust identification method based on computer vision according to claim 1, wherein the method comprises the following steps: and performing morphological opening operation on the binarized image, eliminating fine objects, corroding and then expanding the fine objects to obtain foreground and background images, and merging to obtain a mask image.
6. The building dust identification method based on computer vision according to claim 1, wherein the method comprises the following steps: according to a mask diagram obtained by morphological opening operation, the mask diagram is used as a threshold value mark image of a building dust recognition watershed algorithm; the watershed algorithm is that the threshold value marked image sequences the pixel gray level of the building dust-raising target area from low to high, then inundation is realized from the pixel value to high, the boundary point of the highest gray value of the marked image is the watershed, and the boundary point is the edge information of the building dust-raising target area; and (5) segmenting and extracting the dust of the target building by using a threshold marked watershed algorithm.
7. The building dust identification method based on computer vision according to claim 1, wherein the method comprises the following steps: and judging whether to output an alarm identification signal by using a pixel area method according to the ratio of the sum of pixel areas for extracting building dust to the total pixel area of the image.
8. The building dust identification method based on computer vision according to claim 1, wherein the method comprises the following steps: judging that building dust exists in the image, storing the image and outputting an identification alarm signal of the building dust.
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